The USPS Postal Standard (Publication 28) provides at least some of the specifications we need for address quality. For example,

“The Postal Service defines a complete address as one that has all the address elements necessary to allow an exact match with the current Postal Service ZIP+4 and City State files to obtain the finest level of ZIP+4 and delivery point codes for the delivery address.”

The next paragraph provides some additional details:

“A standardized address is one that is fully spelled out, abbreviated by using the Postal Service standard abbreviations (shown in this publication) or as shown in the current Postal Service ZIP+4 file.”

A large part of the remainder of the document guides what is valid and what is
not valid, as well as the postal standard abbreviations (as mentioned in the
definition of standardized). So an address must be complete, which by definition
implies that it can be matched with current Postal Service ZIP+4 and City State
files.

This match is to obtain the ZIP+4, so the implication is that
verification means that a complete address matches the USPS files and has the
correct ZIP+4. The address components must be consistent with the postal
standard in terms of valid and invalid values. For example, a street address
cannot have a number that is outside the range of recognized numbers (that is,
if the USPS file says that Main Street goes from 1-104, an address with 109 Main
St is invalid). So validation means that the street address is consistent with
what is documented by the USPS files. Standardization is also defined by the
above reference: it is spelled out, and uses the USPS standard abbreviations.

In turn, the process for address quality would be to:

1) Ensure the address is complete.2) Ensure that the address values are valid by checking it against the
USPS files.3) Verify the address’s ZIP+4 by matching against the USPS fles.4) Standardize the address according to the USPS standardized
abbreviations.

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All data goes bad (up to 25% per year), whether due to data entry errors or the simple fact that consumers change jobs, move, update email addresses, marry, etc. At Melissa, we help companies harness the value of their Big Data, legacy data, and people data (names, addresses, phone numbers, and emails) to drive insight, maintain data quality, and support global intelligence